Overview

Dataset statistics

Number of variables22
Number of observations10754
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory176.0 B

Variable types

Text3
Categorical3
Numeric16

Alerts

yellow cards is highly skewed (γ1 = 38.23386693)Skewed
red cards is highly skewed (γ1 = 63.31158671)Skewed
clean sheets is highly skewed (γ1 = 87.39062027)Skewed
player has unique valuesUnique
appearance has 389 (3.6%) zerosZeros
goals has 4354 (40.5%) zerosZeros
assists has 4539 (42.2%) zerosZeros
yellow cards has 2213 (20.6%) zerosZeros
second yellow cards has 9585 (89.1%) zerosZeros
red cards has 9423 (87.6%) zerosZeros
goals conceded has 9739 (90.6%) zerosZeros
clean sheets has 9800 (91.1%) zerosZeros
minutes played has 405 (3.8%) zerosZeros
days_injured has 4117 (38.3%) zerosZeros
games_injured has 4227 (39.3%) zerosZeros
award has 4773 (44.4%) zerosZeros
current_value has 167 (1.6%) zerosZeros
highest_value has 125 (1.2%) zerosZeros

Reproduction

Analysis started2024-05-28 14:26:22.761239
Analysis finished2024-05-28 14:26:32.006803
Duration9.25 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

player
Text

UNIQUE 

Distinct10754
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:32.057439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length55
Median length49
Mean length36.26167
Min length26

Characters and Unicode

Total characters389958
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10754 ?
Unique (%)100.0%

Sample

1st row/david-de-gea/profil/spieler/59377
2nd row/jack-butland/profil/spieler/128899
3rd row/tom-heaton/profil/spieler/34130
4th row/lisandro-martinez/profil/spieler/480762
5th row/raphael-varane/profil/spieler/164770
ValueCountFrequency (%)
david-de-gea/profil/spieler/59377 1
 
< 0.1%
teden-mengi/profil/spieler/548470 1
 
< 0.1%
vladimir-coufal/profil/spieler/157672 1
 
< 0.1%
donny-van-de-beek/profil/spieler/288255 1
 
< 0.1%
tom-heaton/profil/spieler/34130 1
 
< 0.1%
lisandro-martinez/profil/spieler/480762 1
 
< 0.1%
raphael-varane/profil/spieler/164770 1
 
< 0.1%
harry-maguire/profil/spieler/177907 1
 
< 0.1%
victor-lindelof/profil/spieler/184573 1
 
< 0.1%
phil-jones/profil/spieler/117996 1
 
< 0.1%
Other values (10744) 10744
99.9%
2024-05-28T17:26:32.175331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 43016
 
11.0%
e 33037
 
8.5%
i 32320
 
8.3%
r 30556
 
7.8%
l 28809
 
7.4%
p 23004
 
5.9%
o 21227
 
5.4%
s 17880
 
4.6%
a 17738
 
4.5%
f 11975
 
3.1%
Other values (30) 130396
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 389958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 43016
 
11.0%
e 33037
 
8.5%
i 32320
 
8.3%
r 30556
 
7.8%
l 28809
 
7.4%
p 23004
 
5.9%
o 21227
 
5.4%
s 17880
 
4.6%
a 17738
 
4.5%
f 11975
 
3.1%
Other values (30) 130396
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 389958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 43016
 
11.0%
e 33037
 
8.5%
i 32320
 
8.3%
r 30556
 
7.8%
l 28809
 
7.4%
p 23004
 
5.9%
o 21227
 
5.4%
s 17880
 
4.6%
a 17738
 
4.5%
f 11975
 
3.1%
Other values (30) 130396
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 389958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 43016
 
11.0%
e 33037
 
8.5%
i 32320
 
8.3%
r 30556
 
7.8%
l 28809
 
7.4%
p 23004
 
5.9%
o 21227
 
5.4%
s 17880
 
4.6%
a 17738
 
4.5%
f 11975
 
3.1%
Other values (30) 130396
33.4%

team
Text

Distinct374
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:32.255610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length35
Median length28
Mean length14.561466
Min length4

Characters and Unicode

Total characters156594
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManchester United
2nd rowManchester United
3rd rowManchester United
4th rowManchester United
5th rowManchester United
ValueCountFrequency (%)
fc 2711
 
10.6%
club 635
 
2.5%
united 534
 
2.1%
city 453
 
1.8%
ca 391
 
1.5%
sc 306
 
1.2%
clube 291
 
1.1%
cf 287
 
1.1%
atlético 266
 
1.0%
de 257
 
1.0%
Other values (572) 19491
76.1%
2024-05-28T17:26:32.383274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14868
 
9.5%
a 13024
 
8.3%
e 10961
 
7.0%
o 9241
 
5.9%
n 7987
 
5.1%
r 7870
 
5.0%
i 7795
 
5.0%
l 7733
 
4.9%
t 7533
 
4.8%
C 7248
 
4.6%
Other values (69) 62334
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14868
 
9.5%
a 13024
 
8.3%
e 10961
 
7.0%
o 9241
 
5.9%
n 7987
 
5.1%
r 7870
 
5.0%
i 7795
 
5.0%
l 7733
 
4.9%
t 7533
 
4.8%
C 7248
 
4.6%
Other values (69) 62334
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14868
 
9.5%
a 13024
 
8.3%
e 10961
 
7.0%
o 9241
 
5.9%
n 7987
 
5.1%
r 7870
 
5.0%
i 7795
 
5.0%
l 7733
 
4.9%
t 7533
 
4.8%
C 7248
 
4.6%
Other values (69) 62334
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14868
 
9.5%
a 13024
 
8.3%
e 10961
 
7.0%
o 9241
 
5.9%
n 7987
 
5.1%
r 7870
 
5.0%
i 7795
 
5.0%
l 7733
 
4.9%
t 7533
 
4.8%
C 7248
 
4.6%
Other values (69) 62334
39.8%

name
Text

Distinct10584
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:32.469579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length27
Median length24
Mean length13.327692
Min length2

Characters and Unicode

Total characters143326
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10444 ?
Unique (%)97.1%

Sample

1st rowDavid de Gea
2nd rowJack Butland
3rd rowTom Heaton
4th rowLisandro Martínez
5th rowRaphaël Varane
ValueCountFrequency (%)
lucas 101
 
0.5%
kim 96
 
0.4%
lee 75
 
0.4%
juan 73
 
0.3%
david 63
 
0.3%
gabriel 56
 
0.3%
van 54
 
0.3%
daniel 53
 
0.2%
carlos 53
 
0.2%
josé 51
 
0.2%
Other values (11435) 20709
96.8%
2024-05-28T17:26:32.597322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 15167
 
10.6%
10630
 
7.4%
e 10380
 
7.2%
i 9976
 
7.0%
o 9734
 
6.8%
n 9027
 
6.3%
r 7999
 
5.6%
l 6291
 
4.4%
s 5363
 
3.7%
u 4702
 
3.3%
Other values (85) 54057
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 15167
 
10.6%
10630
 
7.4%
e 10380
 
7.2%
i 9976
 
7.0%
o 9734
 
6.8%
n 9027
 
6.3%
r 7999
 
5.6%
l 6291
 
4.4%
s 5363
 
3.7%
u 4702
 
3.3%
Other values (85) 54057
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 15167
 
10.6%
10630
 
7.4%
e 10380
 
7.2%
i 9976
 
7.0%
o 9734
 
6.8%
n 9027
 
6.3%
r 7999
 
5.6%
l 6291
 
4.4%
s 5363
 
3.7%
u 4702
 
3.3%
Other values (85) 54057
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 15167
 
10.6%
10630
 
7.4%
e 10380
 
7.2%
i 9976
 
7.0%
o 9734
 
6.8%
n 9027
 
6.3%
r 7999
 
5.6%
l 6291
 
4.4%
s 5363
 
3.7%
u 4702
 
3.3%
Other values (85) 54057
37.7%

position
Categorical

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.1 KiB
Defender Centre-Back
1821 
Attack Centre-Forward
1353 
Goalkeeper
1229 
midfield-CentralMidfield
1149 
midfield-DefensiveMidfield
900 
Other values (11)
4302 

Length

Max length26
Median length21
Mean length19.617073
Min length6

Characters and Unicode

Total characters210962
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGoalkeeper
2nd rowGoalkeeper
3rd rowGoalkeeper
4th rowDefender Centre-Back
5th rowDefender Centre-Back

Common Values

ValueCountFrequency (%)
Defender Centre-Back 1821
16.9%
Attack Centre-Forward 1353
12.6%
Goalkeeper 1229
11.4%
midfield-CentralMidfield 1149
10.7%
midfield-DefensiveMidfield 900
8.4%
Defender Right-Back 867
8.1%
Defender Left-Back 807
7.5%
midfield-AttackingMidfield 769
7.2%
Attack-RightWinger 717
 
6.7%
Attack-LeftWinger 714
 
6.6%
Other values (6) 428
 
4.0%

Length

2024-05-28T17:26:32.655327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
defender 3528
22.6%
centre-back 1821
11.7%
attack 1406
 
9.0%
centre-forward 1353
 
8.7%
goalkeeper 1229
 
7.9%
midfield-centralmidfield 1149
 
7.4%
midfield-defensivemidfield 900
 
5.8%
right-back 867
 
5.6%
left-back 807
 
5.2%
midfield-attackingmidfield 769
 
4.9%
Other values (6) 1773
11.4%

Most occurring characters

ValueCountFrequency (%)
e 33765
16.0%
d 17176
 
8.1%
i 17081
 
8.1%
t 15037
 
7.1%
r 13347
 
6.3%
f 12164
 
5.8%
n 11016
 
5.2%
a 10897
 
5.2%
- 9364
 
4.4%
l 8493
 
4.0%
Other values (22) 62622
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 210962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 33765
16.0%
d 17176
 
8.1%
i 17081
 
8.1%
t 15037
 
7.1%
r 13347
 
6.3%
f 12164
 
5.8%
n 11016
 
5.2%
a 10897
 
5.2%
- 9364
 
4.4%
l 8493
 
4.0%
Other values (22) 62622
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 210962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 33765
16.0%
d 17176
 
8.1%
i 17081
 
8.1%
t 15037
 
7.1%
r 13347
 
6.3%
f 12164
 
5.8%
n 11016
 
5.2%
a 10897
 
5.2%
- 9364
 
4.4%
l 8493
 
4.0%
Other values (22) 62622
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 210962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 33765
16.0%
d 17176
 
8.1%
i 17081
 
8.1%
t 15037
 
7.1%
r 13347
 
6.3%
f 12164
 
5.8%
n 11016
 
5.2%
a 10897
 
5.2%
- 9364
 
4.4%
l 8493
 
4.0%
Other values (22) 62622
29.7%

height
Real number (ℝ)

Distinct47
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.24035
Minimum156
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:32.698068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum156
5-th percentile170
Q1176
median181.24035
Q3186
95-th percentile193
Maximum206
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.9698182
Coefficient of variation (CV)0.038456216
Kurtosis-0.33391044
Mean181.24035
Median Absolute Deviation (MAD)5.2403529
Skewness-0.047892306
Sum1949058.8
Variance48.578366
MonotonicityNot monotonic
2024-05-28T17:26:32.741591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
180 744
 
6.9%
185 608
 
5.7%
175 596
 
5.5%
178 594
 
5.5%
183 572
 
5.3%
188 493
 
4.6%
184 458
 
4.3%
182 449
 
4.2%
181.2403529 440
 
4.1%
186 423
 
3.9%
Other values (37) 5377
50.0%
ValueCountFrequency (%)
156 1
 
< 0.1%
159 1
 
< 0.1%
160 15
 
0.1%
161 3
 
< 0.1%
162 6
 
0.1%
163 16
 
0.1%
164 14
 
0.1%
165 42
0.4%
166 44
0.4%
167 85
0.8%
ValueCountFrequency (%)
206 2
 
< 0.1%
204 1
 
< 0.1%
202 6
 
0.1%
200 12
 
0.1%
199 12
 
0.1%
198 24
 
0.2%
197 31
 
0.3%
196 68
0.6%
195 98
0.9%
194 117
1.1%

age
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.041903
Minimum15
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:32.778486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile19
Q122
median26
Q329
95-th percentile34
Maximum43
Range28
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.7776287
Coefficient of variation (CV)0.18345928
Kurtosis-0.48806681
Mean26.041903
Median Absolute Deviation (MAD)4
Skewness0.35397006
Sum280054.63
Variance22.825736
MonotonicityNot monotonic
2024-05-28T17:26:32.813865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
22 841
 
7.8%
23 823
 
7.7%
26 812
 
7.6%
25 772
 
7.2%
24 767
 
7.1%
21 715
 
6.6%
28 678
 
6.3%
27 666
 
6.2%
29 650
 
6.0%
30 617
 
5.7%
Other values (20) 3413
31.7%
ValueCountFrequency (%)
15 6
 
0.1%
16 19
 
0.2%
17 80
 
0.7%
18 224
 
2.1%
19 411
3.8%
20 601
5.6%
21 715
6.6%
22 841
7.8%
23 823
7.7%
24 767
7.1%
ValueCountFrequency (%)
43 1
 
< 0.1%
42 7
 
0.1%
41 6
 
0.1%
40 19
 
0.2%
39 20
 
0.2%
38 36
 
0.3%
37 90
 
0.8%
36 149
1.4%
35 177
1.6%
34 257
2.4%

appearance
Real number (ℝ)

ZEROS 

Distinct108
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.407011
Minimum0
Maximum107
Zeros389
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:32.856802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median35
Q359
95-th percentile80
Maximum107
Range107
Interquartile range (IQR)47

Descriptive statistics

Standard deviation26.526541
Coefficient of variation (CV)0.72861077
Kurtosis-1.0979613
Mean36.407011
Median Absolute Deviation (MAD)23
Skewness0.28755915
Sum391521
Variance703.65735
MonotonicityNot monotonic
2024-05-28T17:26:33.058689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 389
 
3.6%
1 333
 
3.1%
4 225
 
2.1%
17 221
 
2.1%
10 216
 
2.0%
2 213
 
2.0%
5 213
 
2.0%
9 200
 
1.9%
16 195
 
1.8%
7 191
 
1.8%
Other values (98) 8358
77.7%
ValueCountFrequency (%)
0 389
3.6%
1 333
3.1%
2 213
2.0%
3 189
1.8%
4 225
2.1%
5 213
2.0%
6 169
1.6%
7 191
1.8%
8 157
1.5%
9 200
1.9%
ValueCountFrequency (%)
107 3
 
< 0.1%
106 2
 
< 0.1%
105 5
< 0.1%
104 2
 
< 0.1%
103 1
 
< 0.1%
102 5
< 0.1%
101 8
0.1%
100 8
0.1%
99 10
0.1%
98 3
 
< 0.1%

goals
Real number (ℝ)

ZEROS 

Distinct5329
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12555432
Minimum0
Maximum11.25
Zeros4354
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.101944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.045969184
Q30.17226254
95-th percentile0.49357329
Maximum11.25
Range11.25
Interquartile range (IQR)0.17226254

Descriptive statistics

Standard deviation0.23558515
Coefficient of variation (CV)1.8763604
Kurtosis528.80338
Mean0.12555432
Median Absolute Deviation (MAD)0.045969184
Skewness14.261621
Sum1350.2111
Variance0.055500363
MonotonicityNot monotonic
2024-05-28T17:26:33.147453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4354
40.5%
1 10
 
0.1%
0.324909747 8
 
0.1%
0.4 6
 
0.1%
0.230769231 6
 
0.1%
0.426540284 6
 
0.1%
0.25 6
 
0.1%
0.139318885 5
 
< 0.1%
0.271903323 5
 
< 0.1%
0.182555781 5
 
< 0.1%
Other values (5319) 6343
59.0%
ValueCountFrequency (%)
0 4354
40.5%
0.010710461 1
 
< 0.1%
0.011042945 1
 
< 0.1%
0.01173097 1
 
< 0.1%
0.012027262 1
 
< 0.1%
0.012571588 1
 
< 0.1%
0.012612108 1
 
< 0.1%
0.012619181 1
 
< 0.1%
0.012658228 1
 
< 0.1%
0.012818687 1
 
< 0.1%
ValueCountFrequency (%)
11.25 1
< 0.1%
6 1
< 0.1%
4.5 1
< 0.1%
4.090909091 1
< 0.1%
3.461538462 1
< 0.1%
3.333333333 1
< 0.1%
2.903225806 1
< 0.1%
2 2
< 0.1%
1.956521739 1
< 0.1%
1.914893617 1
< 0.1%

assists
Real number (ℝ)

ZEROS 

Distinct5065
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.086977646
Minimum0
Maximum4
Zeros4539
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.196934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.040773226
Q30.1331361
95-th percentile0.30436775
Maximum4
Range4
Interquartile range (IQR)0.1331361

Descriptive statistics

Standard deviation0.14335133
Coefficient of variation (CV)1.6481399
Kurtosis126.35506
Mean0.086977646
Median Absolute Deviation (MAD)0.040773226
Skewness7.353238
Sum935.35761
Variance0.020549605
MonotonicityNot monotonic
2024-05-28T17:26:33.253006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4539
42.2%
0.100783875 6
 
0.1%
1 6
 
0.1%
0.174081238 6
 
0.1%
0.111111111 6
 
0.1%
0.2 6
 
0.1%
0.053989202 5
 
< 0.1%
0.147299509 5
 
< 0.1%
0.148514851 5
 
< 0.1%
0.142857143 5
 
< 0.1%
Other values (5055) 6165
57.3%
ValueCountFrequency (%)
0 4539
42.2%
0.011359334 1
 
< 0.1%
0.011463508 1
 
< 0.1%
0.011554757 1
 
< 0.1%
0.011789363 1
 
< 0.1%
0.012009608 1
 
< 0.1%
0.012017626 1
 
< 0.1%
0.012196775 1
 
< 0.1%
0.012223279 1
 
< 0.1%
0.01243953 2
 
< 0.1%
ValueCountFrequency (%)
4 1
< 0.1%
3.461538462 1
< 0.1%
2.903225806 1
< 0.1%
2.647058824 1
< 0.1%
2.045454545 1
< 0.1%
2 1
< 0.1%
1.956521739 1
< 0.1%
1.914893617 1
< 0.1%
1.764705882 2
< 0.1%
1.730769231 1
< 0.1%

yellow cards
Real number (ℝ)

SKEWED  ZEROS 

Distinct6291
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18975723
Minimum0
Maximum30
Zeros2213
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.298830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.053191489
median0.15025042
Q30.24827586
95-th percentile0.46016206
Maximum30
Range30
Interquartile range (IQR)0.19508437

Descriptive statistics

Standard deviation0.43238792
Coefficient of variation (CV)2.2786374
Kurtosis2270.7485
Mean0.18975723
Median Absolute Deviation (MAD)0.097618838
Skewness38.233867
Sum2040.6492
Variance0.18695931
MonotonicityNot monotonic
2024-05-28T17:26:33.343366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2213
 
20.6%
0.142857143 16
 
0.1%
0.333333333 16
 
0.1%
0.25 15
 
0.1%
0.125 14
 
0.1%
0.2 13
 
0.1%
0.5 12
 
0.1%
0.1 12
 
0.1%
1 12
 
0.1%
0.117647059 11
 
0.1%
Other values (6281) 8420
78.3%
ValueCountFrequency (%)
0 2213
20.6%
0.009584665 1
 
< 0.1%
0.009868421 1
 
< 0.1%
0.010033445 1
 
< 0.1%
0.012048193 1
 
< 0.1%
0.012508687 1
 
< 0.1%
0.013100437 1
 
< 0.1%
0.013513514 1
 
< 0.1%
0.013827009 1
 
< 0.1%
0.014285714 1
 
< 0.1%
ValueCountFrequency (%)
30 1
< 0.1%
12.85714286 1
< 0.1%
11.25 1
< 0.1%
10 1
< 0.1%
8.181818182 1
< 0.1%
7.5 2
< 0.1%
6 2
< 0.1%
5 1
< 0.1%
4.736842105 2
< 0.1%
4.285714286 1
< 0.1%

second yellow cards
Real number (ℝ)

ZEROS 

Distinct1095
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0046656669
Minimum0
Maximum1
Zeros9585
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.388553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.027551592
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.025232003
Coefficient of variation (CV)5.4080165
Kurtosis511.28355
Mean0.0046656669
Median Absolute Deviation (MAD)0
Skewness17.963089
Sum50.174582
Variance0.00063665399
MonotonicityNot monotonic
2024-05-28T17:26:33.433321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9585
89.1%
0.025034771 3
 
< 0.1%
0.021464345 3
 
< 0.1%
0.021387833 3
 
< 0.1%
0.036363636 3
 
< 0.1%
0.030727211 2
 
< 0.1%
0.017737485 2
 
< 0.1%
0.034377387 2
 
< 0.1%
0.012710069 2
 
< 0.1%
0.067975831 2
 
< 0.1%
Other values (1085) 1147
 
10.7%
ValueCountFrequency (%)
0 9585
89.1%
0.01017524 1
 
< 0.1%
0.010679957 1
 
< 0.1%
0.010784901 1
 
< 0.1%
0.01079784 1
 
< 0.1%
0.011009174 1
 
< 0.1%
0.011038881 1
 
< 0.1%
0.011242973 1
 
< 0.1%
0.011306533 1
 
< 0.1%
0.011464968 1
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.927835052 1
< 0.1%
0.559006211 1
< 0.1%
0.538922156 1
< 0.1%
0.520231214 1
< 0.1%
0.491803279 1
< 0.1%
0.486486486 1
< 0.1%
0.436893204 1
< 0.1%
0.428571429 1
< 0.1%
0.361445783 1
< 0.1%

red cards
Real number (ℝ)

SKEWED  ZEROS 

Distinct1219
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0068260697
Minimum0
Maximum6.9230769
Zeros9423
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.475247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.032220555
Maximum6.9230769
Range6.9230769
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.081142661
Coefficient of variation (CV)11.887171
Kurtosis5047.2959
Mean0.0068260697
Median Absolute Deviation (MAD)0
Skewness63.311587
Sum73.407554
Variance0.0065841315
MonotonicityNot monotonic
2024-05-28T17:26:33.519427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9423
87.6%
0.039232781 3
 
< 0.1%
0.018572018 3
 
< 0.1%
0.018820577 3
 
< 0.1%
0.019810698 3
 
< 0.1%
0.081818182 3
 
< 0.1%
0.042816365 3
 
< 0.1%
0.018529957 3
 
< 0.1%
0.032656023 2
 
< 0.1%
0.048179872 2
 
< 0.1%
Other values (1209) 1306
 
12.1%
ValueCountFrequency (%)
0 9423
87.6%
0.009963467 1
 
< 0.1%
0.010314004 1
 
< 0.1%
0.010738575 1
 
< 0.1%
0.010997067 1
 
< 0.1%
0.011038881 1
 
< 0.1%
0.011042945 1
 
< 0.1%
0.01119403 1
 
< 0.1%
0.011302273 1
 
< 0.1%
0.011463508 1
 
< 0.1%
ValueCountFrequency (%)
6.923076923 1
< 0.1%
2.5 1
< 0.1%
2 1
< 0.1%
1.578947368 1
< 0.1%
1.028571429 1
< 0.1%
1 1
< 0.1%
0.957446809 1
< 0.1%
0.756302521 1
< 0.1%
0.666666667 1
< 0.1%
0.629370629 1
< 0.1%

goals conceded
Real number (ℝ)

ZEROS 

Distinct742
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13165547
Minimum0
Maximum9
Zeros9739
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.564384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.2906844
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44233547
Coefficient of variation (CV)3.3597956
Kurtosis28.401938
Mean0.13165547
Median Absolute Deviation (MAD)0
Skewness4.1661033
Sum1415.8229
Variance0.19566067
MonotonicityNot monotonic
2024-05-28T17:26:33.607518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9739
90.6%
1 36
 
0.3%
2 27
 
0.3%
1.5 20
 
0.2%
1.333333333 12
 
0.1%
0.5 10
 
0.1%
1.4 8
 
0.1%
1.666666667 8
 
0.1%
1.2 8
 
0.1%
0.8 8
 
0.1%
Other values (732) 878
 
8.2%
ValueCountFrequency (%)
0 9739
90.6%
0.255681818 1
 
< 0.1%
0.306330837 1
 
< 0.1%
0.328467153 1
 
< 0.1%
0.4 1
 
< 0.1%
0.5 10
 
0.1%
0.502793296 1
 
< 0.1%
0.520231214 1
 
< 0.1%
0.530451866 1
 
< 0.1%
0.5625 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
5.294117647 1
 
< 0.1%
4.205607477 1
 
< 0.1%
4.090909091 1
 
< 0.1%
3.941605839 1
 
< 0.1%
3.75 1
 
< 0.1%
3.529411765 1
 
< 0.1%
3.5 2
 
< 0.1%
3.4 1
 
< 0.1%
3 7
0.1%

clean sheets
Real number (ℝ)

SKEWED  ZEROS 

Distinct635
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.044880899
Minimum0
Maximum90
Zeros9800
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.649498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.28
Maximum90
Range90
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9244373
Coefficient of variation (CV)20.597566
Kurtosis8369.2805
Mean0.044880899
Median Absolute Deviation (MAD)0
Skewness87.39062
Sum482.64919
Variance0.85458432
MonotonicityNot monotonic
2024-05-28T17:26:33.692309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9800
91.1%
0.5 33
 
0.3%
0.333333333 23
 
0.2%
0.25 19
 
0.2%
1 16
 
0.1%
0.2 15
 
0.1%
0.4 14
 
0.1%
0.142857143 11
 
0.1%
0.285714286 11
 
0.1%
0.272727273 9
 
0.1%
Other values (625) 803
 
7.5%
ValueCountFrequency (%)
0 9800
91.1%
0.045454545 1
 
< 0.1%
0.05 1
 
< 0.1%
0.065597668 1
 
< 0.1%
0.069767442 1
 
< 0.1%
0.073051948 1
 
< 0.1%
0.073230269 1
 
< 0.1%
0.076923077 2
 
< 0.1%
0.083333333 4
 
< 0.1%
0.086956522 1
 
< 0.1%
ValueCountFrequency (%)
90 1
< 0.1%
18 2
< 0.1%
10.58823529 1
< 0.1%
9 1
< 0.1%
8.181818182 1
< 0.1%
5.294117647 1
< 0.1%
3.6 1
< 0.1%
3.214285714 1
< 0.1%
2.571428571 1
< 0.1%
2.307692308 1
< 0.1%

minutes played
Real number (ℝ)

ZEROS 

Distinct5036
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2470.7894
Minimum0
Maximum9510
Zeros405
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.734948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q1660
median2101.5
Q33968
95-th percentile6204.35
Maximum9510
Range9510
Interquartile range (IQR)3308

Descriptive statistics

Standard deviation2021.7033
Coefficient of variation (CV)0.81824185
Kurtosis-0.50787143
Mean2470.7894
Median Absolute Deviation (MAD)1582.5
Skewness0.63146904
Sum26570869
Variance4087284.1
MonotonicityNot monotonic
2024-05-28T17:26:33.778898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 405
 
3.8%
90 98
 
0.9%
180 41
 
0.4%
1530 39
 
0.4%
450 37
 
0.3%
360 33
 
0.3%
630 32
 
0.3%
270 30
 
0.3%
900 25
 
0.2%
44 23
 
0.2%
Other values (5026) 9991
92.9%
ValueCountFrequency (%)
0 405
3.8%
1 8
 
0.1%
2 3
 
< 0.1%
3 8
 
0.1%
4 8
 
0.1%
5 7
 
0.1%
6 5
 
< 0.1%
7 6
 
0.1%
8 5
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
9510 1
< 0.1%
9390 1
< 0.1%
9240 1
< 0.1%
9208 1
< 0.1%
9120 1
< 0.1%
9033 1
< 0.1%
9025 1
< 0.1%
8970 1
< 0.1%
8928 1
< 0.1%
8884 1
< 0.1%

days_injured
Real number (ℝ)

ZEROS 

Distinct772
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.96169
Minimum0
Maximum2349
Zeros4117
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.820051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37
Q3181
95-th percentile475
Maximum2349
Range2349
Interquartile range (IQR)181

Descriptive statistics

Standard deviation175.20683
Coefficient of variation (CV)1.4852858
Kurtosis8.9483639
Mean117.96169
Median Absolute Deviation (MAD)37
Skewness2.3891449
Sum1268560
Variance30697.432
MonotonicityNot monotonic
2024-05-28T17:26:33.862777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4117
38.3%
14 72
 
0.7%
7 51
 
0.5%
10 51
 
0.5%
21 44
 
0.4%
31 44
 
0.4%
19 43
 
0.4%
18 42
 
0.4%
35 42
 
0.4%
42 40
 
0.4%
Other values (762) 6208
57.7%
ValueCountFrequency (%)
0 4117
38.3%
1 1
 
< 0.1%
2 22
 
0.2%
3 19
 
0.2%
4 36
 
0.3%
5 21
 
0.2%
6 39
 
0.4%
7 51
 
0.5%
8 30
 
0.3%
9 39
 
0.4%
ValueCountFrequency (%)
2349 1
< 0.1%
1570 1
< 0.1%
1555 1
< 0.1%
1456 1
< 0.1%
1365 1
< 0.1%
1307 1
< 0.1%
1290 1
< 0.1%
1268 1
< 0.1%
1232 1
< 0.1%
1222 1
< 0.1%

games_injured
Real number (ℝ)

ZEROS 

Distinct154
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.826297
Minimum0
Maximum339
Zeros4227
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.904982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q324
95-th percentile63
Maximum339
Range339
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.383606
Coefficient of variation (CV)1.4775159
Kurtosis9.2215628
Mean15.826297
Median Absolute Deviation (MAD)5
Skewness2.3475073
Sum170196
Variance546.79304
MonotonicityNot monotonic
2024-05-28T17:26:33.949617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4227
39.3%
2 277
 
2.6%
4 252
 
2.3%
3 250
 
2.3%
1 247
 
2.3%
5 234
 
2.2%
7 211
 
2.0%
6 198
 
1.8%
8 179
 
1.7%
9 169
 
1.6%
Other values (144) 4510
41.9%
ValueCountFrequency (%)
0 4227
39.3%
1 247
 
2.3%
2 277
 
2.6%
3 250
 
2.3%
4 252
 
2.3%
5 234
 
2.2%
6 198
 
1.8%
7 211
 
2.0%
8 179
 
1.7%
9 169
 
1.6%
ValueCountFrequency (%)
339 1
< 0.1%
206 1
< 0.1%
201 1
< 0.1%
191 1
< 0.1%
171 2
< 0.1%
169 1
< 0.1%
168 1
< 0.1%
167 1
< 0.1%
162 1
< 0.1%
158 1
< 0.1%

award
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9607588
Minimum0
Maximum92
Zeros4773
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:33.993651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8
Maximum92
Range92
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.7439364
Coefficient of variation (CV)1.9094324
Kurtosis90.932885
Mean1.9607588
Median Absolute Deviation (MAD)1
Skewness6.5056129
Sum21086
Variance14.017059
MonotonicityNot monotonic
2024-05-28T17:26:34.036958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 4773
44.4%
1 2211
20.6%
2 1223
 
11.4%
3 730
 
6.8%
4 460
 
4.3%
5 323
 
3.0%
6 216
 
2.0%
7 199
 
1.9%
8 121
 
1.1%
9 110
 
1.0%
Other values (36) 388
 
3.6%
ValueCountFrequency (%)
0 4773
44.4%
1 2211
20.6%
2 1223
 
11.4%
3 730
 
6.8%
4 460
 
4.3%
5 323
 
3.0%
6 216
 
2.0%
7 199
 
1.9%
8 121
 
1.1%
9 110
 
1.0%
ValueCountFrequency (%)
92 1
< 0.1%
90 1
< 0.1%
65 1
< 0.1%
57 1
< 0.1%
53 1
< 0.1%
43 1
< 0.1%
42 1
< 0.1%
38 1
< 0.1%
37 2
< 0.1%
36 2
< 0.1%

current_value
Real number (ℝ)

ZEROS 

Distinct128
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3622971
Minimum0
Maximum1.8 × 108
Zeros167
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:34.079635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50000
Q1300000
median800000
Q33000000
95-th percentile18000000
Maximum1.8 × 108
Range1.8 × 108
Interquartile range (IQR)2700000

Descriptive statistics

Standard deviation9095409.9
Coefficient of variation (CV)2.5104838
Kurtosis63.339432
Mean3622971
Median Absolute Deviation (MAD)700000
Skewness6.4534585
Sum3.896143 × 1010
Variance8.2726481 × 1013
MonotonicityNot monotonic
2024-05-28T17:26:34.125818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000 473
 
4.4%
300000 456
 
4.2%
500000 441
 
4.1%
50000 422
 
3.9%
1500000 412
 
3.8%
2000000 412
 
3.8%
400000 406
 
3.8%
200000 387
 
3.6%
600000 349
 
3.2%
100000 333
 
3.1%
Other values (118) 6663
62.0%
ValueCountFrequency (%)
0 167
 
1.6%
10000 3
 
< 0.1%
25000 255
2.4%
50000 422
3.9%
75000 170
1.6%
100000 333
3.1%
125000 112
 
1.0%
150000 253
2.4%
175000 82
 
0.8%
200000 387
3.6%
ValueCountFrequency (%)
180000000 1
 
< 0.1%
170000000 1
 
< 0.1%
150000000 1
 
< 0.1%
120000000 1
 
< 0.1%
110000000 3
 
< 0.1%
100000000 4
 
< 0.1%
90000000 2
 
< 0.1%
85000000 5
< 0.1%
80000000 10
0.1%
75000000 8
0.1%

highest_value
Real number (ℝ)

ZEROS 

Distinct156
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6152606
Minimum0
Maximum2 × 108
Zeros125
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size84.1 KiB
2024-05-28T17:26:34.172894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50000
Q1450000
median1500000
Q35000000
95-th percentile30000000
Maximum2 × 108
Range2 × 108
Interquartile range (IQR)4550000

Descriptive statistics

Standard deviation13389876
Coefficient of variation (CV)2.1762935
Kurtosis37.24447
Mean6152606
Median Absolute Deviation (MAD)1325000
Skewness5.0694773
Sum6.6165125 × 1010
Variance1.7928879 × 1014
MonotonicityNot monotonic
2024-05-28T17:26:34.222315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000 470
 
4.4%
1500000 417
 
3.9%
2000000 411
 
3.8%
50000 362
 
3.4%
3000000 351
 
3.3%
500000 340
 
3.2%
2500000 329
 
3.1%
5000000 321
 
3.0%
400000 296
 
2.8%
4000000 295
 
2.7%
Other values (146) 7162
66.6%
ValueCountFrequency (%)
0 125
 
1.2%
10000 2
 
< 0.1%
25000 200
1.9%
50000 362
3.4%
75000 123
 
1.1%
100000 239
2.2%
125000 77
 
0.7%
150000 180
1.7%
175000 66
 
0.6%
200000 221
2.1%
ValueCountFrequency (%)
200000000 1
 
< 0.1%
180000000 2
 
< 0.1%
170000000 1
 
< 0.1%
160000000 1
 
< 0.1%
150000000 8
0.1%
130000000 1
 
< 0.1%
120000000 3
 
< 0.1%
110000000 5
 
< 0.1%
100000000 16
0.1%
90000000 16
0.1%

position_encoded
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.1 KiB
2
3528 
3
3095 
4
2902 
1
1229 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3528
32.8%
3 3095
28.8%
4 2902
27.0%
1 1229
 
11.4%

Length

2024-05-28T17:26:34.264425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T17:26:34.299348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 3528
32.8%
3 3095
28.8%
4 2902
27.0%
1 1229
 
11.4%

Most occurring characters

ValueCountFrequency (%)
2 3528
32.8%
3 3095
28.8%
4 2902
27.0%
1 1229
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3528
32.8%
3 3095
28.8%
4 2902
27.0%
1 1229
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3528
32.8%
3 3095
28.8%
4 2902
27.0%
1 1229
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3528
32.8%
3 3095
28.8%
4 2902
27.0%
1 1229
 
11.4%

winger
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.1 KiB
0
7447 
1
3307 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7447
69.2%
1 3307
30.8%

Length

2024-05-28T17:26:34.334820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T17:26:34.363436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7447
69.2%
1 3307
30.8%

Most occurring characters

ValueCountFrequency (%)
0 7447
69.2%
1 3307
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7447
69.2%
1 3307
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7447
69.2%
1 3307
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7447
69.2%
1 3307
30.8%

Interactions

2024-05-28T17:26:31.280212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.095910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.753352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.230230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.786998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.253679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.793274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.385524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.876737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.366822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.986105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.511851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.047760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.560753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:30.739301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:24.842853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:25.853184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.444200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.936210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:30.290220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:23.241998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.840660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.316039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.871711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:30.348688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.877958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.448881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:30.382064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:23.994341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.483829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.018751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.531787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.144965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.628165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.121945image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.628138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.265605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.766602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.303120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.834104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.497481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.014850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.583841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.470631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.022507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.511134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.045756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.561286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.173167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.663017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.150385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.657055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.294585image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.797029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.333589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.866564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.526110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.047754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.615596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.505583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.050147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.538728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.072512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.591024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.201859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.694861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.179496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.792283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.323294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.849866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.362897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.898489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.554570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.079752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.647927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.534839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.079834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.566692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.100022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.621340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.230810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.723319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.208493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.820587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.352386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.880863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.395029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.060110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.583616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.111190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.679782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.563548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.107930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.594024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.127200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.650912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.258737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.752243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T17:26:27.849293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.382464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.910627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.425286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.090892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.612100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.142774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.720210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.658621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.139319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.625387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.157874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.684545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.290720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.783438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.269860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.881281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.416005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.944300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.459105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.125676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.643309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.178508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.751751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.686696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.166015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.652173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.184460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.720270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.319049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.811510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.298693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.911756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.443718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.974830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.488691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.157963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.671269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.209114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.786478image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:23.719771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.197661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:24.752999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.219858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:25.755228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.351553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:26.843294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.331937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:27.945857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:28.476711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.010109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:29.524865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.193621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:30.705348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T17:26:31.244259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-05-28T17:26:31.846612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-28T17:26:31.948081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

playerteamnamepositionheightageappearancegoalsassistsyellow cardssecond yellow cardsred cardsgoals concededclean sheetsminutes playeddays_injuredgames_injuredawardcurrent_valuehighest_valueposition_encodedwinger
0/david-de-gea/profil/spieler/59377Manchester UnitedDavid de GeaGoalkeeper189.032.01040.0000000.0000000.0095850.00000.01.2172520.335463939042513150000007000000010
1/jack-butland/profil/spieler/128899Manchester UnitedJack ButlandGoalkeeper196.030.0150.0000000.0000000.0690180.00000.01.2423310.207055130451058115000002200000010
2/tom-heaton/profil/spieler/34130Manchester UnitedTom HeatonGoalkeeper188.037.040.0000000.0000000.0000000.00000.00.6164380.924658292697844600000600000010
3/lisandro-martinez/profil/spieler/480762Manchester UnitedLisandro MartínezDefender Centre-Back175.025.0820.0280900.0561800.2247190.00000.00.0000000.0000006408175229500000005000000020
4/raphael-varane/profil/spieler/164770Manchester UnitedRaphaël VaraneDefender Centre-Back191.030.0630.0178890.0178890.0536670.00000.00.0000000.00000050312385121400000008000000020
5/harry-maguire/profil/spieler/177907Manchester UnitedHarry MaguireDefender Centre-Back194.030.0680.0377990.0000000.3023940.01890.00.0000000.0000004762148271250000007000000020
6/victor-lindelof/profil/spieler/184573Manchester UnitedVictor LindelöfDefender Centre-Back187.028.0700.0000000.0329010.1151530.00000.00.0000000.0000005471951910150000003500000020
7/phil-jones/profil/spieler/117996Manchester UnitedPhil JonesDefender Centre-Back185.031.080.0000000.0000000.2163460.00000.00.0000000.000000416932169720000002000000020
8/teden-mengi/profil/spieler/548470Manchester UnitedTeden MengiDefender Centre-Back186.021.0340.0000000.0000000.1305290.00000.00.0000000.0000002758471302000000200000020
9/luke-shaw/profil/spieler/183288Manchester UnitedLuke ShawDefender Left-Back178.027.0740.0153740.1691150.3536040.00000.00.0000000.0000005854443704350000004200000021
playerteamnamepositionheightageappearancegoalsassistsyellow cardssecond yellow cardsred cardsgoals concededclean sheetsminutes playeddays_injuredgames_injuredawardcurrent_valuehighest_valueposition_encodedwinger
10744/alessandro-lopane/profil/spieler/819840Western Sydney WanderersAlessandro Lopanemidfield-AttackingMidfield181.24035319.0320.0709780.0000000.2129340.00.0000000.00.0126800012500015000030
10745/brandon-borrello/profil/spieler/293592Western Sydney WanderersBrandon BorrelloAttack-RightWinger178.00000027.0450.3593370.1382060.2487710.00.0000000.00.03256400611700000120000041
10746/amor-layouni/profil/spieler/208880Western Sydney WanderersAmor LayouniAttack-RightWinger191.00000030.0120.4428040.3321030.2214020.00.0000000.00.081369110500000100000041
10747/yeni-ngbakoto/profil/spieler/111053Western Sydney WanderersYeni N'GbakotoAttack-RightWinger173.00000031.0350.2916670.2083330.1250000.00.0416670.00.02160000500000300000041
10748/jarrod-carluccio/profil/spieler/749273Western Sydney WanderersJarrod CarluccioAttack-RightWinger178.00000022.0320.2267000.0000000.2833750.00.0000000.00.0158800020000020000041
10749/aidan-simmons/profil/spieler/867763Western Sydney WanderersAidan SimmonsAttack-RightWinger181.24035320.0160.1759530.0879770.2639300.00.0000000.00.01023000750007500041
10750/kusini-yengi/profil/spieler/708099Western Sydney WanderersKusini YengiAttack Centre-Forward190.00000024.0260.3726710.1863350.1863350.00.0000000.00.0144910218030000030000040
10751/nathanael-blair/profil/spieler/1023268Western Sydney WanderersNathanael BlairAttack Centre-Forward181.24035319.0200.3750000.0000000.1875000.00.0000000.00.0960000500005000040
10752/zachary-sapsford/profil/spieler/703657Western Sydney WanderersZachary SapsfordAttack Centre-Forward181.24035320.0170.3121390.1040460.0000000.00.1040460.00.0865000500005000040
10753/alexander-badolato/profil/spieler/957230Western Sydney WanderersAlexander BadolatoAttack Centre-Forward170.00000018.0210.0000000.0000000.0860420.00.0000000.00.01046000250002500040